Mapping memory-biased dynamics with compact models reveals overlapping communities in large networks
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arXiv
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| Hauptverfasser: | , , , , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866914151623819264 |
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| author | Lindström, Maja Sahasrabuddhe, Rohit Holmgren, Anton Blöcker, Christopher Edler, Daniel Rosvall, Martin |
| author_facet | Lindström, Maja Sahasrabuddhe, Rohit Holmgren, Anton Blöcker, Christopher Edler, Daniel Rosvall, Martin |
| contents | Many real-world systems, from social networks to protein-protein interactions and species distributions, exhibit overlapping flow-based communities that reflect their functional organisation. However, reliably identifying such overlapping flow-based communities requires higher-order relational data, which are often unavailable. To address this challenge, we capitalise on the flow model underpinning the representation-learning algorithm node2vec and model higher-order flows through memory-biased random walks on first-order networks. Instead of simulating these walks, we model their higher-order dynamic constraints with compact models and control model complexity with an information-theoretic approach. Using the map equation framework, we identify overlapping modules in the resulting higher-order networks. Our compact-model approach proves robust across synthetic benchmark networks, reveals interpretable overlapping communities in empirical networks, and scales to large networks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2304_05775 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Mapping memory-biased dynamics with compact models reveals overlapping communities in large networks Lindström, Maja Sahasrabuddhe, Rohit Holmgren, Anton Blöcker, Christopher Edler, Daniel Rosvall, Martin Physics and Society Many real-world systems, from social networks to protein-protein interactions and species distributions, exhibit overlapping flow-based communities that reflect their functional organisation. However, reliably identifying such overlapping flow-based communities requires higher-order relational data, which are often unavailable. To address this challenge, we capitalise on the flow model underpinning the representation-learning algorithm node2vec and model higher-order flows through memory-biased random walks on first-order networks. Instead of simulating these walks, we model their higher-order dynamic constraints with compact models and control model complexity with an information-theoretic approach. Using the map equation framework, we identify overlapping modules in the resulting higher-order networks. Our compact-model approach proves robust across synthetic benchmark networks, reveals interpretable overlapping communities in empirical networks, and scales to large networks. |
| title | Mapping memory-biased dynamics with compact models reveals overlapping communities in large networks |
| topic | Physics and Society |
| url | https://arxiv.org/abs/2304.05775 |